Name
Abstract | ||
|---|---|---|
Synthetic aperture radar (SAR) is a remote sensing technology that can truly operate 24/7. It's an all-weather system that can operate at any time except in the most extreme conditions. By making multiple passes over a wide area, a SAR can provide surveillance over a long time period. For high level processing it is convenient to segment and classify the SAR images into objects that identify various terrains and man-made structures that we call “static features.” In this paper we concentrate on automatic road segmentation. This not only serves as a surrogate for finding other static features, but road detection in of itself is important for aligning SAR images with other data sources. In this paper we introduce a novel SAR image product that captures how different regions decorrelate at different rates. We also show how a modified Kolmogorov-Smirnov test can be used to model the static features even when the independent observation assumption is violated. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/CVPRW.2015.7301309 | 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
Keywords | Field | DocType |
multipass single-pol synthetic aperture radar imagery,remote sensing technology,SAR images,automatic road segmentation,Kolmogorov-Smirnov test | Computer vision,Radar imaging,Pattern recognition,Segmentation,Computer science,Side looking airborne radar,Synthetic aperture radar,Inverse synthetic aperture radar,Image segmentation,Artificial intelligence,Speckle noise,Synthetic aperture sonar | Conference |
Volume | Issue | ISSN |
2015 | 1 | 2160-7508 |
Citations | PageRank | References |
0 | 0.34 | 28 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mark W. Koch | 1 | 92 | 10.60 |
| Mary M. Moya | 2 | 101 | 16.90 |
| Jim G. Chow | 3 | 0 | 0.34 |
| Jeremy Goold | 4 | 0 | 0.34 |
| Rebecca Malinas | 5 | 3 | 1.13 |